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Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm

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Abstract

In this paper, a novel spectral-spatial hyperspectral image classification method has been proposed by designing hierarchical subspace switch ensemble learning algorithm. First, the hyperspectral images are processed by fast bilateral filtering to get the spatial features. The spectral features and spatial features are combined to form the initial feature set. Second, Hierarchical instance learning based on iterative means clustering method is designed to obtain hierarchical instance space. Third, random subspace method (RSM) is used for sampling the features and samples, thereby forming multiple sub sample set. After that, semi-supervised learning (S2L) is applied to choose test samples for improving classification performance without touching the class labels. Then, micro noise linear dimension reduction (mNLDR) is used for dimension reduction. Afterwards, ensemble multiple kernels SVM(EMK_SVM) are used for stable classification results. Finally, final classification results are obtained by combining classification results with voting strategy. Experimental results on real hyperspectral scenes demonstrate that the proposed method can effectively improve the classification performance apparently.

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References

  1. Cui M, Prasad S (2015) Class-dependent sparse representation classifier for robust hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(5):2683–2695

    Article  Google Scholar 

  2. Li C et al (2015) Hyperspectral image denoising using the robust low-rank tensor recovery. J Opt Soc Amer A 32(9):1604–1612

    Article  Google Scholar 

  3. Muller-Karger F et al (2013) Satellite remote sensing in support of an integrated ocean observing system. IEEE Geosci Remote Sens Lett 1(4):8–18

    Article  Google Scholar 

  4. Leeuwen M, Nieuwenhuis M (2010) Retrieval of forest structural parameters using Li DAR remote sensing. Eur J Forest Res 129(4):749–770

    Article  Google Scholar 

  5. Zhang C, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13(6):693–712

    Article  Google Scholar 

  6. S Amini S, Homayouni A (2014) Safari, Semi-supervised classification of hyperspectral image using random forest algorithm, Geoscience and Remote Sensing Symposium (IGARSS), pp 2866–2869

  7. Archibald R, Fann G (2007) Feature selection and classification of hyperspectral images with support vector machines. IEEE Geosci Remote Sens Lett 4(4):674–677

    Article  Google Scholar 

  8. Chen Y, Nasrabadi NM, Tran TD (2013) Hyperspectral image classification via kernel sparse representation. IEEE Trans Geosci Remote Sens 51(1):217–231

    Article  Google Scholar 

  9. Chen C et al (2014) Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens 6(6):5795–5814

    Article  Google Scholar 

  10. Ghamisi P, Benediktsson JA (2015) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett 12(2):309–313

    Article  Google Scholar 

  11. Nguyen TM, Wu QMJ (2012) Gaussian-mixture-model-based spatial neighborhood relationships for pixel labeling problem. IEEE Trans Syst Man Cybern 42(1):193–202

    Article  Google Scholar 

  12. Chen C et al (2014) Spectral-spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification. IEEE J Sel Top Appl Earth Observ 7(4):1047–1059

    Article  Google Scholar 

  13. Fauvel M, Benediktsson JA, Chanussot J, Sveinsson JR (2008) spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans Geosci Remote Sens 46(11):3804–3814

    Article  Google Scholar 

  14. Tarabalka Y, Chanussot J, Benediktsson JA (2010) Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit 43(7):2367–2379

    Article  Google Scholar 

  15. Fang L, Shutao Li, Kang X, Benediktsson JA (2014) Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation. IEEE Trans Geosci Remote Sens 52(12):7738–7749

    Article  Google Scholar 

  16. Kang X, Li S, Benediktsson JA (2014) Feature extraction of hyperspectral images with image fusion and recursive filtering. IEEE Trans Geosci Remote Sens 52(6):3742–3752

    Article  Google Scholar 

  17. Lin CH, Tsai JS, Chiu CT (2010) Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans Image Process 19(9):2307–2320

    Article  MathSciNet  Google Scholar 

  18. Chaudhury KN, Dabhade SD (2016) Fast and provably accurate bilateral filtering. IEEE Trans Image Process 25(6):2519–2528

    Article  MathSciNet  Google Scholar 

  19. Ye Z, Li H, Song Y, Wang J, Benediktsson JA (2016) A novel semi-supervised learning framework for hyperspectral image classification. INT Jwavelets Multi 14(02):15–31. https://doi.org/10.1142/S0219691316400051

    MathSciNet  MATH  Google Scholar 

  20. Ma L, Ma A, Ju C, Li X (2016) Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification. Pattern Recogn Lett 83:133–142

    Article  Google Scholar 

  21. Samat A, Du P, Liu S, Li J (2014) Ensemble extreme learning machines for hyperspectral image classification. IEEE J-STARS 7(4):1060–1069

    Google Scholar 

  22. Zhang E, Zhang X, Jiao L, Li L, Hou B (2016) Spectral–spatial hyperspectral image ensemble classification via joint sparse representation. Pattern Recogn 59(C):42–54

    Article  Google Scholar 

  23. Liu T, Gu Y, Jia X, Benediktsson JA, Chanussot J (2016) Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification. IEEE Trans Geosci Remote Sens 54(12):7351–7365

    Article  Google Scholar 

  24. Gu Y, Liu T, Jia X, Benediktsson JA, Chanussot J (2017) Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens 54(6):3235–3247

    Article  Google Scholar 

  25. Li C, Ho HH, Kuo BC, et al. (2015) A semi-supervised feature extraction based on supervised and fuzzy-based linear discriminant analysis for hyperspectral image classification. Appl Math Inf Sci 9(1L):81–87

    Article  Google Scholar 

  26. Appice A, Guccione P, Malerba D (2017) A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data [J]. Pattern Recogn 63:229–245

    Article  Google Scholar 

  27. Appice A, Guccione P, Malerba D (2016) Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system [J]. Mach Learn 103(3):343–375

    Article  MathSciNet  Google Scholar 

  28. Guccione P, Mascolo L, Appice A (2015) Iterative hyperspectral image classification using spectral–spatial relational features[J]. IEEE Trans Geoscience Remote Sens 53 (7):3615– 3627

    Article  Google Scholar 

  29. Pasolli E, Melgani F, Tuia D et al (2014) SVM Active learning approach for image classification using spatial information [J]. IEEE Trans Geoscience Remote Sens 52(4):2217–2233

    Article  Google Scholar 

  30. Pasolli E, Yang HL, Crawford MM (2016) Active-metric Learning for classification of remotely sensed hyperspectral images [J]. IEEE Trans Geoscience Remote Sens 54(4):1925–1939

    Article  Google Scholar 

  31. Pan B, Shi Z, Xu X (2017) Mugnet: deep learning for hyperspectral image classification using limited samples [J]. ISPRS J Photogrammetry Remote Sens

  32. Chen Y, Lin Z, Zhao X et al. (2017) Deep learning-based classification of hyperspectral data [J]. IEEE J Selected Topics Appl Earth Observations Remote Sens 7(6):2094–2107

    Article  Google Scholar 

  33. Makantasis K, Karantzalos K, Doulamis A et al (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks[J]. pp 4959–4962

  34. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision. IEEE, pp 839–846

  35. Chaudhury KN, Dabhade SD (2016) Fast and provably accurate bilateral filtering. IEEE Trans Image Process 25(6):2519–2528

    Article  MathSciNet  Google Scholar 

  36. Li CH et al (2015) A semi-supervised feature extraction based on supervised and fuzzy-based linear discriminant analysis for hyperspectral image classification. Appl Math Inf Sci 9(1L):81–87

    Article  Google Scholar 

  37. Yuan H et al (2014) Hyperspectral image classification based on regularized sparse representation. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2174–2182

    Article  Google Scholar 

  38. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790

    Article  Google Scholar 

  39. Rodriguez JJ, Kuncheva LI (2009) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630

    Article  Google Scholar 

  40. Kang X, Li S, Benediktsson JA (2014) Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans Geosci Remote Sens 52(5):2666–2677

    Article  Google Scholar 

  41. Kang X et al (2015) Intrinsic image decomposition for feature extraction of hyperspectral images. IEEE Trans Geosci Remote Sens 53(4):2241–2253

    Article  Google Scholar 

  42. Toksöz MA, Ulusoy I (2016) Hyperspectral image classification via basic thresholding classifier. IEEE Trans Geosci Remote Sens 54(7):4039–4051

    Article  Google Scholar 

  43. Sahadevan AS et al (2016) Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines. J Appl Remote Sens 10(2):025004-1-025004-17

    Article  Google Scholar 

  44. Zhang X, Song Q, Liu R, Wang W, Jiao L Modified co-training with spectral and spatial views for semi-supervised hyperspectral image classifica

Download references

Acknowledgements

This research is funded by National Natural Science Foundation of China NSFC (No: 61771080, 61108086, 91438104, 61571069, 81601970, 61501065), Basic and Advanced Research Project in Chongqing(cstc2016jcyjA0043, cstc2016jcyjA0134,cstc2016jcyjA0064), Chongqing Social Undertaking and People’s Livelihood Guarantee Science and Technology innovation Special Foundation (cstc2016shmszx40002), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR)(201800011), the Fundamental Research Funds for the Central Universities (106112017CDJQJ168817), and Hitachi (China) Research & Development Corporation and Scientific.

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Authors and Affiliations

  1. College of Communication Engineering of Chongqing University, Chongqing, 400030, People’s Republic of China

    Yongming Li, Tingjie Xie, Pin Wang, Jie Wang, Shujun Liu, Xichuan Zhou & Xinzheng Zhang

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  1. Yongming Li
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  2. Tingjie Xie
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Correspondence to Yongming Li.

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Li, Y., Xie, T., Wang, P. et al. Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm. Appl Intell 48, 4128–4148 (2018). https://doi.org/10.1007/s10489-018-1200-8

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